Fuzzy uncertainty modelling for project planning; application to helicopter maintenance

Maintenance is an activity of growing interest specially for critical systems. Particularly, aircraft maintenance costs are becoming an important issue in the aeronautical industry. Managing an aircraft maintenance center is a complex activity. One of the difficulties comes from the numerous uncertainties that affect the activity and disturb the plans at short and medium term. Based on a helicopter maintenance planning and scheduling problem, we study in this paper the integration of uncertainties into tactical and operational multiresource, multi-project planning (respectively Rough Cut Capacity Planning and Resource Constraint Project Scheduling Problem). Our main contributions are in modelling the periodic workload on tactical level considering uncertainties in macro-tasks work contents, and modelling the continuous workload on operational level considering uncertainties in tasks durations. We model uncertainties by a fuzzy/possibilistic approach instead of a stochastic approach since very limited data are available. We refer to the problems as the Fuzzy RoughCut Capacity Problem (FRCCP) and the Fuzzy Resource Constraint Project Scheduling Problem (RCPSP).We apply our models to helicopter maintenance activity within the frame of the Helimaintenance project, an industrial project approved by the French Aerospace Valley cluster which aims at building a center for civil helicopter maintenance

Advanced periodic maintenance scheduling methods for aircraft lifecycle management

This paper reviews existing methods and techniques addressing the problem of maintenance support throughout the life cycle for high value manufacturing products such as aircrafts. As part of this doctorate research the analysis of current methods of maintenance scheduling was conducted. In order to contribute to a more comprehensive solution, an advanced approach (algorithm) of periodic maintenance is presented. The authors believe that this approach will reduce the cost of maintenance of high value manufacturing products. The algorithm based on constraint programming methods is briefly presented and the future research directions are discussed

A Mathematical Model for HVLV Systems Scheduling and Optimization With Periodic Preventive Maintenance Using (max, +) Algebra

International audienceThe High-Variety, Low-Volume (HVLV) scheduling problem is one of the most arduous combinatorial optimization problems. This paper considers an interesting formulation of the HVLV scheduling problem using (max, +) algebra while periodic Preventive Maintenance (PM) is considered. Maintenance is time based since activities are periodically fixed: maintenance is required after a periodic time interval (all periods are equals on each machine). In this paper, the maintenance tasks of machines are controllable.The jobs and the maintenance operations are scheduled simultaneously. Also, the maintenance operations are scheduled between each other, so that a regular criterion is optimized. To generate feasible schedules, constrained decision variables are incorporated into the (max, +) model. The validity of the proposed approach is illustrated by simulation examples

Single machine scheduling with periodic machine availability

In this paper we address the problem of scheduling jobs on a single machine with cyclical machine availability periods. In this problem, the scheduling horizon is composed of periods where the machine is available followed by other periods where no operation can be performed. In the literature, the problem is denoted as scheduling with periodic maintenance, as it is usually assumed that these unavailability periods are employed to perform maintenance activities. Another situation is the one inspiring our research, i.e. the need of completing manufacturing operations within a shift. More specifically, we focus the single machine scheduling problem with makespan objective subject to periodic machine availability. There are several contributions proposing approximate procedures due to the NP-hardness shown for the problem. However, we are not aware of a computational evaluation among these procedures. Furthermore, the problem is similar to the classical bin packing problem, so it is of interest to explore the relation between both problems. In this paper, we address these two issues, and propose new approximate solution procedures for the problem.Ministerio de Ciencia e Innovación DPI2016-80750-

Integrated Production-Distribution Planning with Considering Preventive Maintenance

The preventive maintenance activity is important thing in production system especially for a continuous production process, for example in fertilizer industry. Therefore, it has to be considered in production-distribution planning. This paper considers the interval of production facility’s preventive maintenance in production-distribution planning of multi echelon supply chain system which consists of a manufacturer with a continuous production process, a distribution center, a number of distributors and a number of retailers. The problem address in this paper is how to determine coordinated productiondistribution policies that considers the interval of production facility’s preventive maintenance, and customer demand only occurred at retailers and it fluctuates by time. Based on model of Santoso, et al. (2007), using the periodic review inventory model and a coordinated production and replenishment policies that are decided by central planning office and it must be obeyed by all entities of multi-echelon supply chain, the integrated production-distribution planning model is developed to determine the production and replenishment policies of all echelon in the supply chain system in order to minimize total system cost during planning horizon. Total system cost consists of set-up/ordering cost, maintenance cost, holding cost, outsourcing cost and transportation cost at all of entities. With considering preventive maintenance and there is one production run over the planning horizon, the replenishment cycle at distribution center, distributors and retailers that are found out are greater than the basic model. Also, the multiplication of replenishment cycle at distribution center in production cycle that is found out is greater than the basic model but the multiplication of replenishment cycle at retailers in its distributor are smaller than the basic model

Integrating Line Planning for Construction Sites into Periodic Timetabling via Track Choice

We consider maintenance sites for urban rail systems, where unavailable tracks typically require changes to the regular timetable, and often even to the line plan. In this paper, we present an integrated mixed-integer linear optimization model to compute an optimal line plan that makes best use of the available tracks, together with a periodic timetable, including its detailed routing on the tracks within the stations. The key component is a flexible, turn-sensitive event-activity network that allows to integrate line planning and train routing using a track choice extension of the Periodic Event Scheduling Problem (PESP). Major goals are to maintain as much of the regular service as possible, and to keep the necessary changes rather local. Moreover, we present computational results on real construction site scenarios on the S-Bahn Berlin network. We demonstrate that this integrated problem is indeed solvable on practically relevant instances

Generic Bayesian network models for making maintenance decisions from available data and expert knowledge

To maximise asset reliability cost-effectively, maintenance should be scheduled based on the likely deterioration of an asset. Various statistical models have been proposed for predicting this, but they have important practical limitations. We present a Bayesian network model that can be used for maintenance decision support to overcome these limitations. The model extends an existing statistical model of asset deterioration, but shows how (1) data on the condition of assets available from their periodic inspection can be used, (2) failure data from related groups of asset can be combined using judgement from experts and (3) expert knowledge of the deterioration’s causes can be combined with statistical data to adjust predictions. A case study of bridges on the rail network in Great Britain (GB) is presented, showing how the model could be used for the maintenance decision problem, given typical data likely to be available in practice

Condition Based Maintenance Optimization of Multi-Equipment Manufacturing Systems by Combining Discrete Event Simulation and Multiobjective Evolutionary Algorithms

Modern industrial engineers are continually faced with the challenge of meeting increasing demands for high quality products while using a reduced amount of resources. Since systems used in the production of goods and deliveries of services constitute the vast portion of capital in most industries, maintenance of such systems is crucial (Oyarbide-Zubillaga, Goti, & Sánchez 2008). Several studies compiled by Mjema (2002) show that maintenance costs represent from 3 to 40 % out of the total product cost (with an average value of a 28%). Within maintenance, the Condition-Based Maintenance (CBM) techniques are very important. Nevertheless, and comparing it to the Preventive Maintenance (PM) optimization problem, relatively few papers related to CBM have been developed: According to Aven (1996), one of the reasons to justify this fact is that CBM models are usually by its nature rather sophisticated compared to the more traditional replacement models. Within this maintenance strategy, Das & Sarkar (1999) distinguish two CBM subtypes, On-Condition Maintenance (OCM) and Condition Monitoring (CMT). OCM is based on periodic inspections, while CMT performs a continuous monitoring on the hardware through instrumentation. Considering the described context, this paper focuses on the problem of CMT optimisation in a manufacturing environment, with the objective of determining the optimal CMT deterioration levels beyond which PM activities should be applied under cost and profit criteria in a multi-equipment system. The initiative considers the interaction of production, work in process material, quality and maintenance aspects. In this work the suitability of discrete event simulation to model or modify complex system models is combined with the aptitude that multiobjective evolutionary algorithms have shown to deal with multiobjective problems to develop a maintenance management and optimisation approach. An application case where the activities applied on a system that produces hubcaps for the car maker industry is performed, showing the quantitative benefits of adopting the detailed approach

A Methodology for Continuous Monitoring of Rail Corrugation on Subway Lines Based on Axlebox Acceleration Measurements

Rail corrugation is a degradation phenomenon that manifests as a quasi-periodic irregularity on the running surface of the rail. It is a critical problem for urban railway lines because it induces ground-borne vibrations transmitted to the buildings near the infrastructure, causing complaints from the inhabitants. A typical treatment to mitigate the rail corrugation problem is the periodic grinding of the rails, performed by dedicated vehicles. The scheduling of rail maintenance is particularly critical because it can be performed only when the service is interrupted. A procedure for the continuous monitoring of rail corrugation is proposed, based on axlebox acceleration measurements. The rail irregularity is estimated from the measured acceleration by means of a frequency domain model of vertical dynamics of the wheel–rail interaction. The results obtained by using two different methods (a state-of-the-art method and a new one) are compared. Finally, the study of the evolution of the power content of the rail irregularity enables the identification of the track sections where corrugation is developing and rail grinding is necessary